Data Science Curriculum

One of the most important components of a quantitative finance curriculum is learning how to manipulate and interpret large, sometimes “messy” datasets to solve problems. Regardless of the job title, many MSCF graduates report their work has some aspect of data science embedded in it. We understand the importance of learning data science skill sets in a way that’s directly applicable to your future career, whether you’re working in finance or other industries.

When considering what type of master’s program you want to pursue, it’s important to understand the content offered by each curriculum. Many one-year statistics or data science programs focus on building very important soft skills as well as computing skills, using languages like Python or other tools that help address issues within big data. Of course, these programs include statistical methods coursework and often have a course project associated with them.

All of these topics, from soft skills to technical course projects, are covered within the MSCF data science curriculum. Because MSCF students are highly talented academically, our curriculum allows us to teach more advanced topics on top of just the theoretical foundation required for roles involving data science.

Though MSCF coursework is certainly skewed toward a financial application, our students’ academic rigor allows us to address some of the most challenging parts of data science, such as how to work with messy data and fit it into a more classic machine learning approach. We start by focusing on fundamental, foundational material, helping our students understand how these foundations can extend to many applications, including those within finance. Our coursework then builds on different machine learning methods and their properties so that students can understand not just how a method could apply to a specific problem, but whether it even makes sense to do so.

With five core and two elective data science courses, MSCF is unmatched among quant finance master’s degrees in preparing students for success in this ever-expanding field. In the last semester of the program, students can participate in a machine learning-focused capstone project, working with an outside sponsor on a data-rich project that presents significant, real world challenges that need to be solved.